one noise variable, logistic regression

## [1] "*************************************************************"
## [1] "one noise variable, logistic regression"
## [1] "bSigmaBest 32"
## [1] "naive effects model"
## [1] "one noise variable, logistic regression naive effects model fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.8068  -1.0493   0.5770   0.9415   2.5190  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.18447    0.05074   3.635 0.000277 ***
## n1           2.20269    0.13545  16.262  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2772.6  on 1999  degrees of freedom
## Residual deviance: 2256.7  on 1998  degrees of freedom
## AIC: 2260.7
## 
## Number of Fisher Scoring iterations: 6
## 
## [1] "one noise variable, logistic regression naive effects model train mean deviance 1.62786601580457"

## [1] "one noise variable, logistic regression naive effects model test mean deviance 3.71500787962648"

## [1] "effects model, sigma= 32"
## [1] "one noise variable, logistic regression effects model, sigma= 32 fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.214  -1.204   1.141   1.151   1.329  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.06419    0.04941   1.299  0.19387   
## n1           0.03702    0.01249   2.964  0.00304 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2772.6  on 1999  degrees of freedom
## Residual deviance: 2763.7  on 1998  degrees of freedom
## AIC: 2767.7
## 
## Number of Fisher Scoring iterations: 3
## 
## [1] "one noise variable, logistic regression Noised 32 train mean deviance 1.99361582203067"

## [1] "one noise variable, logistic regression Noised 32 test mean deviance 2.00456032886049"

## [1] "effects model, jacknifed"
## [1] "one noise variable, logistic regression effects model, jackknifed fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.3619  -1.1570   0.9662   1.1980   1.2169  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -0.04838    0.04731  -1.023  0.30650   
## n1          -0.06366    0.01954  -3.258  0.00112 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2772.6  on 1999  degrees of freedom
## Residual deviance: 2761.8  on 1998  degrees of freedom
## AIC: 2765.8
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one noise variable, logistic regression jackknifed train mean deviance 1.99219567357296"

## [1] "one noise variable, logistic regression jackknifed test mean deviance 2.00542702505421"

## [1] "********"
## [1] "one noise variable, logistic regression AverageManyNoisedModels"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.999   2.000   2.001   2.001   2.001   2.005 
## [1] 0.001104472
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.999   2.001   2.003   2.004   2.005   2.023 
## [1] 0.003992458
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.522   3.872   4.044   4.041   4.204   4.570 
## [1] 0.2316713
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.999   2.001   2.002   2.002   2.003   2.010 
## [1] 0.002048446
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression ObliviousModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.999   2.000   2.000   2.001   2.001   2.006 
## [1] 0.00106767
## [1] "********"
## [1] "*************************************************************"

one variable, logistic regression

## [1] "*************************************************************"
## [1] "one variable, logistic regression"
## [1] "bSigmaBest 4"
## [1] "naive effects model"
## [1] "one variable, logistic regression naive effects model fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1243  -1.1809   0.4704   1.1554   1.5778  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.4731     0.0542    8.73   <2e-16 ***
## x1            3.1777     0.2114   15.03   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2434.7  on 1998  degrees of freedom
## AIC: 2438.7
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable, logistic regression naive effects model train mean deviance 1.75629049009229"

## [1] "one variable, logistic regression naive effects model test mean deviance 1.74484448505444"

## [1] "effects model, sigma= 4"
## [1] "one variable, logistic regression effects model, sigma= 4 fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0306  -1.1685   0.5217   1.1472   1.6129  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.45010    0.05285   8.516   <2e-16 ***
## x1           3.02567    0.20065  15.079   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2447.3  on 1998  degrees of freedom
## AIC: 2451.3
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable, logistic regression Noised 4 train mean deviance 1.76536175195365"

## [1] "one variable, logistic regression Noised 4 test mean deviance 1.75674472607196"

## [1] "effects model, jacknifed"
## [1] "one variable, logistic regression effects model, jackknifed fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0811  -1.1892   0.4966   1.1600   1.5642  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.45308    0.05326   8.508   <2e-16 ***
## x1           2.99703    0.20478  14.636   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2460.2  on 1998  degrees of freedom
## AIC: 2464.2
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable, logistic regression jackknifed train mean deviance 1.77463669725858"

## [1] "one variable, logistic regression jackknifed test mean deviance 1.746225629925"

## [1] "********"
## [1] "one variable, logistic regression AverageManyNoisedModels"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.737   1.760   1.772   1.772   1.780   1.823 
## [1] 0.0165008
## [1] "********"
## [1] "********"
## [1] "one variable, logistic regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.737   1.761   1.772   1.772   1.779   1.819 
## [1] 0.01604833
## [1] "********"
## [1] "********"
## [1] "one variable, logistic regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.737   1.760   1.772   1.772   1.780   1.823 
## [1] 0.01688741
## [1] "********"
## [1] "********"
## [1] "one variable, logistic regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.744   1.761   1.774   1.776   1.785   1.895 
## [1] 0.02226869
## [1] "********"
## [1] "********"
## [1] "one variable, logistic regression ObliviousModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.975   1.984   1.986   1.986   1.988   1.993 
## [1] 0.003336783
## [1] "********"
## [1] "*************************************************************"

one variable plus noise variable, logistic regression

## [1] "*************************************************************"
## [1] "one variable plus noise variable, logistic regression"
## [1] "bSigmaBest 7"
## [1] "naive effects model"
## [1] "one variable plus noise variable, logistic regression naive effects model fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5658  -0.9120   0.3055   0.8035   2.7112  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.68760    0.06161   11.16   <2e-16 ***
## x1           3.18452    0.23641   13.47   <2e-16 ***
## n1           2.45247    0.15572   15.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 1990.5  on 1997  degrees of freedom
## AIC: 1996.5
## 
## Number of Fisher Scoring iterations: 6
## 
## [1] "one variable plus noise variable, logistic regression naive effects model train mean deviance 1.43587337720022"

## [1] "one variable plus noise variable, logistic regression naive effects model test mean deviance 3.54303901440774"

## [1] "effects model, sigma= 7"
## [1] "one variable plus noise variable, logistic regression effects model, sigma= 7 fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0177  -1.1986   0.5326   1.0979   1.6982  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.48151    0.05739   8.391   <2e-16 ***
## x1           3.03989    0.20336  14.948   <2e-16 ***
## n1           0.02137    0.01518   1.407    0.159    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2454.7  on 1997  degrees of freedom
## AIC: 2460.7
## 
## Number of Fisher Scoring iterations: 3
## 
## [1] "one variable plus noise variable, logistic regression Noised 7 train mean deviance 1.77067562081881"

## [1] "one variable plus noise variable, logistic regression Noised 7 test mean deviance 1.78620816972643"

## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, logistic regression effects model, jackknifed fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2012  -1.1757   0.5026   1.1657   1.5936  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.42346    0.05493   7.710 1.26e-14 ***
## x1           3.00699    0.20534  14.644  < 2e-16 ***
## n1          -0.05278    0.02435  -2.167   0.0302 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2455.4  on 1997  degrees of freedom
## AIC: 2461.4
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable plus noise variable, logistic regression jackknifed train mean deviance 1.77119992923416"

## [1] "one variable plus noise variable, logistic regression jackknifed test mean deviance 1.77521675815884"

## [1] "********"
## [1] "one variable plus noise variable, logistic regression AverageManyNoisedModels"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.744   1.760   1.773   1.773   1.782   1.820 
## [1] 0.01470923
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.741   1.763   1.773   1.772   1.780   1.809 
## [1] 0.01399957
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.159   3.480   3.590   3.612   3.768   4.147 
## [1] 0.2109597
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.747   1.776   1.787   1.791   1.800   1.925 
## [1] 0.02397365
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression ObliviousModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.977   1.984   1.986   1.986   1.988   1.992 
## [1] 0.003011528
## [1] "********"
## [1] "*************************************************************"